Image manipulation applications are often used to edit the color, contrast, and tonal distributions of images for stylistic reasons. These edits can be performed by modifying properties such as color hue, tint, saturation, and contrast using image manipulation applications. One way of altering the appearance of an image to achieve a desired change to the image involves a style transfer. Transferring a “style” involves applying one or more color-changing or contrast-changing filters or other operations to an image. For instance, style transfers executed by image manipulation applications use examples of stylized images (also known as “style exemplars”) to identify a style that is to be applied to an image. The color and contrast of a given style exemplar controls how the image manipulation application alters the color and contrast of the input image to generate an output image. Color schemes or other style features may be modified in the input image to match the color scheme or other style features of a style exemplar. Using style exemplars can allow users to intuitively identify color schemes, contrast schemes, or style feature of interest that are to be applied to an input image.
For example, FIG. 1 is a block diagram depicting an example of modifying style features of an input image 102 using a style exemplar 106. An image manipulation application performs one or more stylization processes 104 that globally or locally transform color information and contrast information of the input image 102 in accordance with a style exemplar 106. In one example, the input image 102 is transformed into the output image 108 by applying a stylization process 104 that transfers textural features of the style exemplar 106 to the input image 102.
Conventional style-transfer networks present disadvantages. For instance, style-transfer processes are trained on a specific resolution of the style exemplars. But if a trained style-transfer network is applied to images with resolutions that are different from that specific resolution, sub-optimal style transfers will result. For instance, applying a style-transfer network trained with a style exemplar guide with a size of 256×256 to higher-resolution images would generate results whose texture scale is smaller than that of the artistic style. In another example, conventional style-transfer processes often fail to accurately transfer small, intricate textures (e.g., brushwork), which are found in many kinds of artwork, from style exemplars to high-resolution input images. Thus, conventional style-transfer solutions are often unsatisfactory for transferring a wide variety of artistic styles from a style exemplar to an input image.